Details on pre-processing.

Ítems similars

• Details on microneurography experiments.
  per: Alina Troglio (13165866)
  Publicat: (2025)
• Different feature sets are extracted through domain-agnostic dimension reduction as well as through spike approaches, using the raw waveform and computed features on the waveforms. The feature sets include methodologies, such as principal component analysis (PCA) and the features from the spike sorting approach based on shape, phase, and distribution features (SS-SPDF) by Caro-Martín et al. [11].
  per: Alina Troglio (13165866)
  Publicat: (2025)
• Overview of the data collection, including the number of active tracks, the class distributions, and the number of total spikes. Additionally, we included the spike numbers for each feature set. The classes are mostly equally distributed. Datasets labeled with A are from the lab in Aachen and datasets labeled with B are from Bristol.
  per: Alina Troglio (13165866)
  Publicat: (2025)
• Performance of sorting algorithms within the SpikeInterface framework for datasets A1 and A5. SpyKING Circus 2 and MountainSort5 were applied to two microneurography recordings (datasets A1 and A5). While both algorithms detected varying numbers of units and identified only a subset of the ground truth spikes, the primary issue was the failure to distinguish between fibers: spikes from both fibers were consistently merged into a single unit.
  per: Alina Troglio (13165866)
  Publicat: (2025)
• Clustering performance across increasing numbers of PCA components for A1. To assess whether additional components improved separability, PCA was extended up to eight components beyond the commonly used first two or three. Clustering performance was evaluated using Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), and V-measure.
  per: Alina Troglio (13165866)
  Publicat: (2025)